Spatial Variability of Chondrichthyes in the Northern Mediterranean

Home , Bathytoshia, Squatina squatina, Tetronarce

Mediterranean demersal resources and ecosystems: Scientia Marina 83S1 25 years of MEDITS trawl surveys December 2019, 81-100, Barcelona (Spain) M.T. Spedicato, G. Tserpes, B. Mérigot and ISSN-L: 0214-8358 E. Massutí (eds) https://doi.org/10.3989/scimar.04998.23A

Spatial variability of Chondrichthyes in the northern Mediterranean

Maria C. Follesa 1, Martina F. Marongiu 1, Walter Zupa 2, Andrea Bellodi 1, Alessandro Cau 1, Rita Cannas 1, Francesco Colloca 3, Mirko Djurovic 4, Igor Isajlovic 5, Angélique Jadaud 6, Chiara Manfredi 7, Antonello Mulas 1, Panagiota Peristeraki 8,9, Cristina Porcu 1, Sergio Ramirez-Amaro 10, Francisca Salmerón Jiménez 11, Fabrizio Serena 12, Letizia Sion 13, Ioannis Thasitis 14, Angelo Cau 1, Pierluigi Carbonara 2 1 Department of Life and Environmental Science, University of Cagliari, Via T. Fiorelli 1, Cagliari, Italy. (MCF) (Corresponding author). E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0001-8320-9974 (MFM) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-0582-8865 (AB) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-9017-1692 (AlC) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0003-4082-7531 (RC) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0001-6627-4053 (AM) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0003-1635-691X (CP) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0003-2649-6502 (AnC) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-6897-2127 2 COISPA Tecnologia & Ricerca, Via dei Trulli 18-20, Bari, Italy. (WZ) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-2058-8652 (PC) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-2529-2535 3 Stazione Zoologica A. Dohrn, Villa comunale, Napoli, Italy. (FC) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-0574-2893 4 Institute of Marine Biology, University of Montenegro, PBox 69, 85330 Kotor, Montenegro. (MD) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-4352-9911 5 Institute of Oceanography and Fisheries, Set. I Mestrovica 63, 21000 Split, Croatia. (II) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0001-7101-9575 6 MARBEC-IFREMER, IRD, Université Montpellier 2, CNRS, Avenue Jean Monnet, CS 30171, 34203 Sète Cedex, France. (AJ) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0001-6858-3570 7 Laboratorio di Biologia Marina e Pesca di Fano, Università di Bologna, viale Adriatico 1/n, 61311 Fano, Italy. (CM) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-2852-4856 8 Hellenic Center for Marine Research, Institute of Marine Biological Resources and Inland Waters, 71003, Heraklion, Greece. (PP) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-8608-078X 9 University of Crete, Biology Department, Stavrakia, Heraklion, Crete. 10 Instituto Español de Oceanografía, Centro Oceanográfico de Baleares, Moll de Ponent s/n, 07015 Palma, Spain. (SR-A) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-0298-0749 11 Instituto Español de Oceanografía, Centro Oceanográfico de Málaga, Puerto Pesquero s/n, 29640 Fuengirola, Spain. (FSJ) E-mail: [email protected] iD: https://orcid.org/0000-0001-8907-5444 12 National Research Council, Institute for Coastal Marine Environment, Via Vaccara 61, 91026 Mazara del Vallo, TP Italy. (FS) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0003-1428-8124 13 Department of Biology, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy. (LS) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-0308-1841 14 Department of Fisheries and Marine Research, 101 Vithleem str., 1416 Nicosia, Cyprus. (IT) E-mail: [email protected]. ORCID iD: https://orcid.org/0000-0002-0940-2212

Summary: Thanks to the availability of the MEDITS survey data, a standardized picture of the occurrence and abundance of demersal Chondrichthyes in the northern Mediterranean has been obtained. During the spring-summer period between 2012 and 2015, 41 Chondrichthyes, including 18 sharks (5 orders and 11 families), 22 batoids (3 orders and 4 families) and 1 chimaera, were detected from several geographical sub-areas (GSAs) established by the General Fisheries Commission for the Mediterranean. Batoids had a preferential distribution on the continental shelf (10-200 m depth), while shark species were more frequent on the slope (200-800 m depth). Only three species, the Carcharhiniformes Galeus melastomus and Scylio- rhinus canicula and the Torpediniformes Torpedo marmorata were caught in all GSAs studied. On the continental shelf, the Rajidae family was the most abundant, being represented in primis by Raja clavata and then by R. miraletus, R. polystigma and R. asterias. The slope was characterized by the prevalence of G. melastomus in all GSAs, followed by S. canicula, E. spinax and Squalus blainville. Areas under higher fishing pressure, such as the Adriatic Sea and the Spanish coast (with the exception of the Balearic Islands), show a low abundance of chondrichthyans, but other areas with a high level of fishing 82 • M.C. Follesa et al.

pressure, such as southwestern Sicily, show a high abundance, suggesting that other environmental drivers work together with fishing pressure to shape their distribution. Results of generalized additive models highlighted that depth is one of the most important environmental drivers influencing the distribution of both batoid and shark species, although temperature also showed a significant influence on their distribution. The approach explored in this work shows the possibility of producing maps modelling the distribution of demersal chondrichthyans in the Mediterranean that are useful for the management and conservation of these species at a regional scale. However, because of the vulnerability of these species to fishing exploitation, fishing pressure should be further incorporated in these models in addition to these environmental drivers. Keywords: fish; Chondrichthyes; bottom trawl surveys; distribution; abundance; Mediterranean. Variabilidad espacial de Chondrichthyes en el norte del Mediterráneo Resumen: Gracias a la disponibilidad de los datos de campanas de pesca experimental MEDITS, se ha realizado una imagen estandarizada de la presencia y abundancia de Chondrichthyes demersales en el norte del Mediterráneo. Durante el período primavera-verano entre 2012 y 2015, se detectaron 41 Chondrichthyes, incluidos 18 tiburones (5 órdenes y 11 familias), 22 batoides (3 órdenes y 4 familias) y 1 quimera, de varias Subáreas Geográficas (GSA) establecidas por la Comisión General de Pesca para el Mediterráneo en la zona. Los batoides tuvieron una distribución preferencial en la plataforma continental (10-200 m de profundidad), mientras que las especies de tiburones fueron más frecuentes en la ladera (200-800 m de profundidad). Solo tres especies, Carcharhiniformes Galeus melastomus, Scyliorhinus canicula y Torpediniformes Torpedo marmorata fueron capturadas en todos los GSA estudiados. En la plataforma continental, la familia Rajidae fue la más abundante, representada en primer lugar por Raja clavata y luego por R. miraletus, R. polystigma y R. asterias. La pendiente se caracterizó por la prevalencia de G. melastomus en todos los GSA, seguido de S. canicula, E. spinax y Squalus blainville. Las áreas bajo mayor presión de pesca, como el Mar Adriático y la costa española (con la excepción de las Islas Baleares), registran una baja abundancia de condricthianos, pero otras áreas con un alto nivel de presión de pesca, como el suroeste de Sicilia, presentan una gran abundancia de estas especies. Sugiere que otros impulsores ambientales trabajen juntos con la presión pesquera para dar forma a su distribución. Los resultados de los modelos aditivos generalizados resaltaron que la profundidad es uno de los factores ambientales más importantes que influyen en la distribución de las especies de batoides y tiburones, aunque la temperatura también mostró una influencia significativa en su distribución. El enfoque explorado en este trabajo muestra la posibilidad de producir mapas que modelen la distribución de los condrictios demersales en el Me- diterráneo, útiles para el manejo y la conservación de estas especies a escala regional. Sin embargo, teniendo en cuenta la vulnerabilidad de estas especies a la explotación pesquera, la presión pesquera debería incorporarse a estos modelos, además de estos factores ambientales. Palabras clave: Chondrichthyes; campanas de pesca experimental de arrastre; distribución; abundancia; mar Mediterráneo. Citation/Cómo citar este artículo: Follesa M.C., Marongiu M.F., Zupa W., Bellodi A., Cau A., Cannas R., Colloca F., Djurovic M., Isajlovic I., Jadaud A., Manfredi C., Mulas A., Peristeraki P., Porcu C., Ramirez-Amaro S., Salmerón Jiménez F., Serena F., Sion L., Thasitis I., Cau A., Carbonara P. 2019. Spatial variability of Chondrichthyes in the northern Mediter- ranean. Sci. Mar. 83S1: 81-100. https://doi.org/10.3989/scimar.04998.23A Editor: E. Massutí. Received: March 15, 2018. Accepted: September 12, 2019. Published: November 15. 2019. Copyright: © 2019 CSIC. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

INTRODUCTION rakers (Fowler et al. 2002, Clarke et al. 2006, Lack and Sant 2009). The landings of sharks and rays reported to Chondrichthyes are one of the oldest and most the Food and Agriculture Organization of the United ecologically diverse vertebrate lineages. They ap- Nations increased to a peak in 2003 and declined by peared about 420 million years ago occupying the 20% afterwards (FAO 2010, Frodella et al. 2016). upper levels of aquatic food webs (Compagno 1990, However, recent global data show an increase of 42% Kriwet et al. 2008). Today, they have a conspicuous of the trade in shark meat (Dent and Clarke 2015) and number of predators playing an important role within the true total catch is considered to be about 3/4 times the marine ecosystem (Stevens et al. 2000, Ferretti greater than reported (Clarke et al. 2006, Worm et al. et al. 2010, Heithaus et al. 2012), so if these species 2013). Most chondrichthyan catches are unregulated disappear, the balance within the aquatic food webs and often misidentified, unrecorded, aggregated or would be compromised. It is well known that overfish- discarded at sea, resulting in a lack of species-specific ing and habitat degradation have altered marine animal landings information (Iglésias et al. 2010, Bornatowski populations (Polidoro et al. 2012), especially those of et al. 2013, Cariani et al. 2017). sharks and rays (Ferretti et al. 2010, Dulvy et al. 2014). Humans have been exploiting marine resources, Cartilaginous fish are intrinsically sensitive to high including sharks, along the Mediterranean coasts since fishing mortality because of their K-selected strategy ancient times (Farrugio et al. 1993). An analysis of (e.g. slow growth, late attainment of sexual maturity, threat levels across all chondrichthyan species has re- long life spans and low fecundity; Stevens et al. 2000). vealed the Mediterranean as a key hotspot of extinction They are frequently captured incidentally, but are often risk (Dulvy et al. 2014). According to the recent assess- retained as valuable by-catch of fisheries that focus ment of the International Union for the Conservation on more productive teleost fish species (Stevens et al. of the Nature (IUCN), 50% of rays (16 of 32 species) 2005). At present, fishing pressure on chondrichthyan and 54% of sharks (22 of 41 species) in the Mediter- species is increasing because of the high, and in some ranean are facing a high risk of extinction, whereas cases rising, value of their meat, fins, livers, and/or gill the only chimaera species (Chimaera monstrosa) is

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 83

Table 1. – Number of MEDITS hauls carried out annually during the period 2012-2015 and analysed in the present study by geographical sub-area (GSA) of the General Fisheries Commission for the Mediterranean, country and depth strata (total, 10-800 m; continental shelf, 10-200 m; slope, 201-800 m). GSA Country Area Total hauls Shelf hauls Slope hauls 1 Spain Northern Alboran 187 71 116 5 Spain Balearic Islands 218 142 76 6 Spain Northern Spain 388 282 106 7 France Gulf of Lions 66 55 11 8 France Corsica 23 10 13 9 Italy Ligurian, northern and central Tyrrhenian seas 120 55 65 10 Italy Central and southern Tyrrhenian seas 70 29 41 11 Italy Sardinian seas 101 63 38 16 Italy Strait of Sicily 120 55 65 17 Italy, Slovenia, Croatia Northern Adriatic 182 169 13 18 Italy, Albania, Montenegro Southern Adriatic 90 63 27 19 Italy Northwestern Ionian 70 27 43 20 Greece Eastern Ionian Sea 50 24 26 22 Greece Argosaronikos, northern and southern Aegean 123 63 60 23 Greece Cretan Sea 21 14 7 25 Cyprus Cyprus 26 19 7 considered as least concern (Dulvy et al. 2016). As Starting from 2010, the General Fisheries Commission highlighted above, the principal driver of decline and for the Mediterranean (GFCM) has adopted measures local extinction is overfishing. Most species are taken to reduce the by-catch of pelagic sharks, has banned as retained valuable by-catch in the bottom trawl and finning practices and has also prohibited the capture the small-scale multispecies fisheries. However, by- and sale of the sharks and rays species listed in Annex catch volumes and species composition of chondrich- II of the Protocol of the Barcelona Convention (FAO thyans are poorly documented in the Mediterranean 2016). However, no effective management measures and data are rarely incorporated into national and in- against the overexploitation of many commercial spe- ternational statistics. Therefore, sharks and rays caught cies have been successfully implemented or enforced as by-catch can be only delivered as an approximation (Vasilakopoulos et al. 2014, Osio et al. 2015). In addi- (Camhi et al. 1998, GFCM 2014). Moreover, despite tion, data on the stock status of sharks and rays remain being banned in 2002, illegal driftnetting is widespread poor or non-existent (Polidoro et al. 2008, Worm et al. throughout the basin: fleets from Algeria, Italy, Mo- 2013, Colloca et al. 2017). rocco, Turkey, among others, continue to fish illegally Numerous studies on chondrichthyan species were with pelagic driftnets (Notarbartolo Di Sciara 2014), made during the first decade of the 21st century in the and this represents an important and largely hidden western (e.g. Carbonell et al. 2003, Moranta et al. source of mortality for pelagic sharks. More recently, 2008a, b), central (e.g. Sion et al. 2004, Abella and a significant decline in chondrichthyan species rich- Serena 2005, Ferretti et al. 2005) and eastern Mediter- ness has been reported throughout the Mediterranean, ranean (e.g. Megalofonou et al. 2005, Tserpes et al. due to increasing threats and local extinctions (Nieto 2006, Peristeraki et al. 2008). However, only a few of et al. 2015, Davidson et al. 2016, Dulvy et al. 2016). them have been updated, mainly those related to Span-

Fig. 1. – Map of the study area showing the sampling stations of the MEDITS surveys during the period 2012-2015 and numbers of the geographical sub-areas (GSAs) established by the General Fisheries Commission for the Mediterranean (GFCM). The names of the GSAs are shown in Table 1.

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A 84 • M.C. Follesa et al. ish (Guijarro et al. 2012, Barría et al. 2015, Ramírez- 25 9.5 47.6 20.8 38.1 Amaro et al. 2015), Italian (Follesa et al. 2011, Ligas GSA et al. 2013, Marongiu et al. 2017) and Greek waters 23 33.3 35.0 16.7 (Maravelias et al. 2012, Tserpes et al. 2013). These GSA studies have been carried out on the northern Mediter- 22 4.4 1.6 52.5 66.7 13.1 ranean coast and very few have been carried out on the GSA southern coast off Africa (e.g. Ordines et al. 2011). 20 4.8 6.7 33.3 33.3 The aim of this work is to give updated information GSA on the distribution and abundance of demersal chon- 19 71 7.9 3.7 0.6 1.0

drichthyans on a wide Mediterranean scale and to com- GSA pare findings among different areas in relation to en- 18 0.8 1.9

vironmental factors. Abundance indices and frequency 81.5 28.9 GSA of occurrence of the species were estimated from data 17 11 obtained from the International Mediterranean Trawl 2.5 0.5 1.9 4.9 25.8 Survey (MEDITS), which aims to produce information GSA 16 on population distribution and demographic structure 1.5 0.5 5.0 1.9 0.5 78.3 31.8 17.3 of demersal species inhabiting the continental shelf and GSA

the upper slope (Bertrand et al. 2002). Thanks to the 11 79 54.1 adoption of a common standardized sampling protocol, GSA this scientific survey provides comparable species- 10 0.9 86.6 10.4 specific data in several parts of the Mediterranean. GSA 9 0.4 0.4 0.9 85.5 30.8 MATERIALS AND METHODS GSA 8 94.2 84.0 The data were collected during the bottom trawl GSA MEDITS surveys, which have been performed annu- 7 95.5 49.2 ally in spring-summer since 1994. This research pro- GSA gramme involves several institutes and teams across 6 70.3 61.2 different geographical sub-areas (GSAs) established GSA by the GFCM. More specifically, data of chondrich- 5 2.8 85.9 73.1 thyans at species level were provided by 16 GSAs GSA from 9 different countries (Spain, France, Italy, Slo- 1 32.1 87.6 65.9 venia, Croatia, Albania, Montenegro, Greece and Cy- GSA prus), covering wide areas of the western, central and eastern Mediterranean (Table 1; Fig. 1). Considering that not all chondrichthyan species were included in (m) 10-800 10-200 10-200 10-200 10-200 10-200 the list of MEDITS target species until 2008, there 200-800 200-800 200-800 200-800 200-800 were no complete data time series available for all Depth range LC LC LC NT NT EN VU study areas. For this reason, only data of four recent VU VU VU DD status years (from 2012 to 2015) were analysed. For the IUCN Greek GSAs (20, 22 and 23), data were available only for the year 2014, as the MEDITS survey was performed only in this year.

As described in the MEDITS Handbook (Anony- Code GALEATL GALUMEL SCYOCAN SCYOSTE GALEGAL MUSTAST MUSTMUS MUSTMED HEPTPER HEXAGRI ALOPVUL mous 2017), these surveys were carried out from May to July, with the exception of 2015 when some Ital- ian GSAs postponed the survey to early autumn due to bureaucratic problems. Samples were taken during

daylight using the experimental bottom trawl GOC-73, LC, least concern; NT, near threatened; VU, vulnerable; EN, endangered; CR, critically endangered) are also shown. with 10 mm cod-end mesh size, which corresponds to a mesh opening of about 20 mm. This gear was specially designed to be highly selective in all sampled areas and depths, allowing a representative sampling of demersal megafauna species. Sampling station selection was (Linnaeus, 1758) (Linnaeus, 1758) Risso, 1827

(Linnaeus, 1758) (Bonnaterre, 1788) based on a stratified sampling scheme including five Rafinesque, 1810

(Bonnaterre, 1788)

(Linnaeus, 1758) (Vaillant, 1888) Cloquet, 1819 (Bonnaterre, 1788)

bathymetric strata: 10-50 m, 51-100 m, 101-200 m, 201-500 m and 501-800 m, except for GSA 17 in which the deepest stratum was not present. The duration of hauls was 30’ at stations at less than 200 m depth and

60’ at deeper stations. The number and weight of all Scyliorhinidae specimens captured were recorded. For each species, the frequency of occurrence within their preferential Species Galeus atlanticus Galeus melastomus Scyliorhinus canicula Scyliorhinus stellaris Galeorhinus galeus Mustelus asterias Mustelus mustelus Mustelus punctulatus Heptranchias perlo Hexanchus griseus Alopias vulpinus Table 2. – Demersal chondrichthyans sampled in MEDITS surveys during the period 2012-2015 and frequency of occurrence (%), esti mated within their preferential depth range (overall, 10-800 m; continental shelf, 10-200 m; slope, 201-800 m), by GFCM geographical sub-area (GSA). The codes of these species and their IUCN status in the Mediterranean (Dulvy et al. 2016; DD, data deficient; Sharks Order Carcharhiniformes Family Pentanchidae Family Family Triakidae Order Hexanchiformes Family Hexanchidae Order Lamniformes Family Alopiidae

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 85 25 3.9 9.5 1.3 8.9 7.8 1.8 7.1 4.8 1.8 57.1 23.8 28.6 51.8 23.8 35.1 27.3 GSA 23 7.1 7.1 7.1 33.3 42.9 50.0 10.0 25.0 57.1 25.0 GSA 3 22 18 1.6 1.6 1.6 9.8 3.3 1.5 3.3 2.3 4.7 8.8 8.8 18.0 47.5 10.3 23.0 10.3 45.0 20.6 22.5 14.0 17.6 GSA 20 6.7 5.6 4.8 2.8 4.8 13.3 46.7 33.3 50.0 14.3 23.8 GSA 19 0.6 7.6 1.9 4.6 2.3 1.7 8.3 6.5 1.7 3.7 63.4 18.6 10.2 13.0 GSA 18 0.9 3.7 0.8 0.8 2.8 8.3 2.8 9.4 6.1 11.1 66.7 48.1 22.6 GSA 2 3 17 3.1 1.6 1.6 0.2 0.8 6.2 0.7 0.1 3.7 13.2 13.0 GSA 2 2 16 1.4 0.5 3.0 1.0 1.4 7.7 1.5 6.5 1.0 0.5 4.4 9.9 2.4 12.3 15.6 51.0 18.4 29.7 24.1 10.3 22.9 42.4 GSA 11 10 2.0 4.0 0.5 6.6 0.4 2.8 8.6 0.8 52.0 15.1 10.4 38.8 18.7 18.3 29.8 39.4 26.1 GSA 10 7.9 0.9 2.4 4.9 1.7 2.6 2.4 6.1 7.2 0.4 2.6 0.3 0.7 54.9 10.4 GSA 9 10 1.8 4.8 2.2 0.4 0.4 6.6 0.9 8.0 6.0 0.2 1.9 5.2 50.0 16.7 12.3 GSA 8 5.8 2.1 9.5 4.8 2.0 2.1 7.1 13.5 61.5 48.1 46.2 82.7 23.8 66.0 76.2 22.3 16.7 GSA 7 2.3 0.8 4.5 2.3 1.4 2.3 1.5 6.0 0.5 1.5 52.3 77.3 15.0 10.2 GSA 6 2.9 2.2 0.3 2.9 9.2 4.8 0.9 3.3 21.7 16.8 GSA 5 1.1 4.4 3.9 5.6 4.3 2.0 1.7 2.2 42.4 21.7 16.2 15.6 33.9 22.9 25.8 16.8 GSA 1 5.1 2.9 0.8 2.7 1.5 4.4 0.4 1.2 9.5 2.7 14.6 59.9 10.1 13.6 25.9 GSA (m) 10-800 10-200 10-200 10-200 10-200 10-200 10-800 10-800 10-200 10-200 10-800 10-200 10-800 10-800 10-200 10-200 10-200 10-200 10-200 200-800 200-800 200-800 200-800 200-800 200-800 200-800 200-800 200-800 200-800 200-800 200-800 Depth range LC LC LC LC LC LC LC LC EN NT NT NT NT NT NT EN NT EN CR CR CR CR CR CR CR DD VU DD VU VU VU status IUCN CENTGRA CENTUYA ETMOSPI SQUABLA SQUTACU CHIMMON DASIPAS PTEOBOV RAJAFUL RAJAMEL RAJANAE RAJAAST RAJABRA RAJACLA RAJAMON RAJAUND RAJAALB TORPMAR TORPTOR Code SCYMLIC OXYNCEN SQUAACA DASICEN DASIVIO MYLIAQU RAJAOXY RAJACIR RAJAMIR RAJAPOL RAJARDA TORPNOB ) LC, least concern; NT, near threatened; VU, vulnerable; EN, endangered; CR, critically endangered) are also shown. (Bloch and Schneider, 1801 (Bonaparte, 1832) (Mitchill, 1815) Bonaparte, 1835 (Geoffroy Saint-Hilaire, 1817) Linnaeus,1758 (Clark, 1926) (Couch, 1838)

(Linnaeus, 1758) Risso, 1810

(Linnaeus, 1758) (Rafinesque, 1810)

(Linnaeus, 1758) (Linnaeus, 1758) (Linnaeus, 1758) Linnaeus, 1758 Cuvier, 1829 (Risso, 1827) (Müller and Henle, 1841) (Linnaeus, 1758)

(Linnaeus, 1758) Regan, 1923 Lafont, 1873 (Lacepède, 1803) Fowler, 1910 Linnaeus, 1758 (Bonnaterre, 1788) Lacepède, 1802 Delaroche, 1809 Linnaeus, 1758 Delaroche, 1809

Species Centrophorus cf. granulosus Centrophorus uyato Dalatias licha Etmopterus spinax Oxynotus centrina Squalus acanthias Squalus blainville Squatina aculeata Chimaera monstrosa Bathytoshia centroura Dasyatis pastinaca Pteroplatytrygon violacea Myliobatis aquila Aetomylaeus bovinus Dipturus oxyrinchus Leucoraja circularis Leucoraja fullonica Leucoraja melitensis Leucoraja naevus Raja asterias Raja brachyura Raja clavata Raja miraletus Raja montagui Raja polystigma Raja radula Raja undulata Rostroraja alba Torpedo marmorata Tetronarce nobiliana Torpedo torpedo Table 2 (Cont.). – Demersal chondrichthyans sampled in MEDITS surveys during the period 2012-2015 and frequency of occurrence ( %), estimated within their preferential depth range (overall, 10-800 m; continental shelf, 10-200 slope, 201-800 m), by GFCM geographical sub-area (GSA). The codes of these species and their I UCN status in the Mediterranean (Dulvy et al. 2016; DD, data deficient; Order Squaliformes Family Centrophoridae Family Dalatidae Family Etmopteridae Family Oxynotidae Family Squalidae Order Squatiniformes Family Squatinidae Chimaeras Order Chimaeriformes Family Chimaeridae Batoids Order Myliobatiformes Family Dasyatidae Family Myliobatidae Order Rajiformes Family Rajidae Order Torpediniformes Family Torpedinidae

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A 86 • M.C. Follesa et al. depth range was calculated by GSAs as percentage of Occurrence hauls where it was present in respect to the total hauls of the stratum during the four-year study period (Table Except in some cases, batoids had a preferential 2). Abundance indexes (biomass index as B in kg km–2 distribution on the continental shelf (10-200 m depth), and density index as D in ind. km–2) were calculated for while sharks were more frequent on the slope (Table each species by GSAs for the following depth ranges: 2). Few species showed a wide distribution in all GSAs overall range (10-800 m), continental shelf (10-200 m) (Fig. 2): the sharks Galeus melastomus, with a frequen- and slope (201-800 m). cy of occurrence between 201 and 800 m depth close to 90% in GSAs 1, 5, 9 and 10 and greater than 90% GAM analysis in GSAs 7 and 8, and Scyliorhinus canicula, with a frequency of occurrence in the whole depth range (10- For each haul, biomass (kg km–2) and density (ind. 800 m) of 73% in GSA 5 and 84% in GSA 8; and the km–2) indexes were calculated for two species groups batoid T. marmorata, with the highest frequency of oc- (sharks and batoids) and then modelled separately by currence (26%) in GSA 1 and the lowest (2%) in GSA generalized additive models (GAMs). The variables to 5 (Table 2). Also in the eastern basin, the two sharks be included in the GAM analysis were checked first for were the most present species, but with frequencies of the variance inflation factor (VIF) and then for the cor- occurrence lower than those recorded in the western relations with the other variables from the Pearson cor- basin: 52%, 33% and 48% in GSAs 22, 23 and 25, re- relation coefficient. The variables were then selected spectively, for G. melastomus and 67%, 35% and 21% considering whether the contribute of each variable in GSAs 22, 23 and 25, respectively, for S. canicula. T. was significant for the improvement of the model’s marmorata showed a frequency of occurrence of 24% fit and also whether the estimation of the variables’ in GSA 20, similar to that obtained in GSA 1, but in smoothing term was significant. the other GSAs of the eastern basin these values did not In each GAM model, the following explanatory exceed 15% (Table 2). variables were used: geographical coordinates (latitude Among Rajiformes, Raja clavata was present in all and longitude), depth, sea surface temperature (SST) GSAs, except in GSA 19, showing the highest frequen- and sea bottom temperature (BotTemp). Depth derived cy of occurrence (66%) in GSA 8 for the western basin from the EMODnet Bathymetry portal (http://www. and in GSAs 20 and 22 (50% and 45%, respectively) emodnet.eu) was post-processed with GIS software for the eastern basin, while the lowest one (0.4%) was (QGIS Development Team 2017) to obtain the bot- recorded in GSA 1 (Table 2, Fig. 2). Raja miraletus tom slope layer. SST and BotTemp data were obtained was representative of the central eastern Mediter- from Copernicus portal (http://marine.copernicus.eu) ranean, while Raja asterias showed higher values of and used as a mean value in spring-summer (the MED- occurrence in the western than in the eastern basin. By ITS survey period) in the four years considered (2012- contrast, Raja undulata was rarely encountered in the 2015). Covariates data were associated with sampling central and eastern part of the Mediterranean, similarly station by mean of QGIS software. to the Carcharhiniformes Mustelus asterias, Mustelus To assess collinearity, the VIF was used, and co- punctulatus and Scyliorhinus stellaris and the Mylio- variates showing VIF values greater than 3 and high batiformes Bathytoshia centroura and Aetomylaeus Pearson’s correlation coefficient (r) (>0.5; absolute bovinus (Table 2). value) were rejected (Zuur et al. 2010). The covari- On the continental shelf, the most present species ates were rejected from the analysis following three were T. marmorata and R. miraletus. On the slope, criteria: (i) if the estimated degrees of freedom of the apart from the already mentioned G. melastomus, the variable was close to 1; (ii) if the interval of confidence Squaliformes Etmopteurs spinax was the second most was zero; and (iii) if the generalized cross-validation sampled species, especially in the western and central (GCV) score (Gu and Wahba 1991) decreased when basins, followed by Dipturus oxyrinchus and Squalus the covariate was eliminated from the model. blainville, which showed a peak in GSA 8, with values Non-normally distributed data were log-trans- of 83% and 48%, respectively (Table 2, Fig. 2). A wide formed. The models were estimated with the mgcv distribution was also shown for the deep water Dala- library from R software (R Development Core Team tias licha and Chimaera monstrosa, while other species 2018, Wood 2006) using the Gaussian distribution were more sparsely scattered throughout the Mediter- from the exponential family. The spatial analysis pre- ranean. In particular, Galeus atlanticus was detected diction was performed using a 0.03° resolution grid and in a very restricted area of the western basin (GSA 1), the resulting maps were produced with QGIS software but with an occurrence of 32%, while Galeorhinus (QGIS Development Team 2017). galeus was only found in GSA 16, with low occurrence (0.5%). In the eastern basin, Leucoraja fullonica and RESULTS Squatina aculeata were rare species (Table 2).

Following the last taxonomic structure suggested Abundance by Eschmeyer et al. (2018), 41 species of chondrichthyans were detected from all the GSAs. They included Considering the overall depth range surveyed (10- 18 sharks (5 orders and 11 families), 22 batoids (3 or- 800 m), density (D) and biomass (B) values of sharks, ders and 4 families) and 1 chimaera (Table 2). batoids and the only chimaera were more variable be-

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 87

Fig. 2. – Frequency of occurrence by GFCM geographical sub-area (GSA) of the most common species of sharks and batoids within their preferential depth range: Scyliorhinus canicula and Raja clavata for the overall range (10-800 m); Raja miraletus and Torpedo marmorata for the continental shelf (10-200 m); and Galeus melastomus, Etmopterus spinax, Dipturus oxyrinchus and Squalus blainville for the slope (200-800 m). White bars, western GSAs; grey bars, central GSAs; black bars, eastern GSAs.

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A 88 • M.C. Follesa et al. GSA 25 D=149±69 B=21±8.8 D=20±9 B=2.7±1.4 D=6±3.2 B=10±6.8 D=1.5±2.6 B=1.8±3.1 D=337±140 B=12±1.5 D=0.5±0.8 B=0.5±1 D=22±36 B=26.7±37 D=15±14 B=10.2±9.7 GSA 23 D=59 B=2.5 D=28 B=4 D=0.8 B=3.7 D=15 B=12.5 GSA 22 D=238 B=26.5 D=401 B=42 D=3 B=0.9 D=33 B=13.3 D=6 B=7.8 D=0.05 B=18 D=1 B=3.7 D=0.3 B=0.3 D=73 B=3.2 D=0.6 B=0.3 D=63 B=15.8 D=101 B=60.5 D=1 B=8.7 GSA 20 D=52 B=5.2 D=13 B=2.3 D=1 B=1.8 D=1 B=0.9 D=2 B=6.4 D=1 B=0.02 D=0.7 B=0.5 D=26 B=13.8 GSA 19 D=116±46 B=19.4±7.4 D=8±6 B=1.3±1 D=2±3 B=0.9±1 D=3±1 B=0.6±0.4 D=0.04±0.07 B=0.03±0.06 D=0.1±0.2 B=0.5±0.9 D=0.04±0.1 B=0.02±0.03 D=0.4±0.2 B=0.5±0.2 D=46±27 B=2±0.7 GSA 18 D=100±50 B=12.7±5 D=80±21 B=9.8±2.7 D=0.4±0.7 B=0.9±1.9 D=6±2 B=1.9±0.7 D=0.1±0.1 B=0.1±0.1 D=0.03±0.1 B=0.1±0.2 D=0.5±0.2 B=1.1±1 D=51±32 B=1.8±0.5 D=1.7±1.9 B=0.7±0.2 GSA 17 D=4±2 B=0.3±0.1 D=81±16 B=14±2.5 D=2.6±2.7 B=1.8±1.4 D=0.2±0.3 B=0.5±0.47 D=2.3±2 B=1.5±2.3 D=7±6 B=9.5±9.9 D=0.03±0.1 B=0.01±0.02 D=0.4±0.7 B=0.02±0.03 D=9±3 B=8.5±6.5 D=0.15±0.2 B=0.3±0.4 GSA 16 D=146±68 B=22.3±9.5 D=60±23 B=8.8±2.9 D=0.1±0.2 B=0.15±0.2 D=0.03±0.05 B=0.5±1 D=5±3 B=4±2.1 D=0.8±0.9 B=0.9±0.8 D=4±2 B=1.5±0.7 D=0.2±0.1 B=0.4±0.3 D=0.02±0.1 B=0.2±0.4 D=1±1.2 B=3.7±3.1 D=0.02±0.04 B=0.06±0.1 D=2±1 B=3.4±1.4 D=21±5 B=1.7±0.6 D=0.2±0.16 B=0.6±0.4 D=61±15 B=25.8±6.5 GSA 11 ) indices estimated in the overall depth stratum (10-800 m, except 10-500 m for GSA 17) all sharks and –2 D=199±64 B=14±5.4 D=403±113 B=27.3±8.6 D=0.5±0.3 B=0.02±0.03 D=0.03±0.1 B=0.02±0.03 D=0.2±0.15 B=0.1±0.04 D=39±10 B=2.2±0.2 D=0.1±0.2 B=0.2±0.4 D=3±2 B=1.9±1.5 GSA 10 D=134±44 B=18.2±5.3 D=4.8±1.7 B=0.8±0.3 D=0.1±0.2 B=0.02±0.03 D=0.5±0.4 B=0.04±0.05 D=0.6±0.2 B=0.6±0.5 D=21±5 B=1.4±0.3 D=5±2 B=0.8±0.3 GSA 9 ) and biomass (B, kg km D=205±35 B=22.4±4.3 D=85±16 B=9.2±2.2 D=0.05±0.1 B=0.2±0.4 D=0.05±0.1 B=0.2±0.4 D=2±0.6 B=0.5±0.1 D=0.05±0.1 B=0.2±0.3 D=0.2±0.19 B=0.7±0.6 D=0.3±0.1 B=0.2±0.1 D=36±11 B=2.8±0.7 D=10±9 B=1.5±1.5 –2 GSA 8 D=1607±436 B=71.6±9.2 D=589±260 B=86.9±42.1 D=0.1±0.2 B=0.2±0.5 D=19±5 B=4.3±1.6 D=0.3±0.6 B=0.8±1.7 D=1±0.2 B=0.6±0.2 D=156±83 B=3.9±1.9 D=0.2±0.3 B=0.1±0.2 D=68±43 B=32.6±14.3 GSA 7 D=297±180 B=40±18.4 D=156±64 B=26.4±9.4 D=15±6 B=5.5±2.5 D=0.03±0.1 B=0.1±0.2 D=9±5.3 B=0.5±0.4 D=0.1±0.2 B=0.3±0.5 D=1.5±1.54 B=0.5±0.7 D=0.1±0.2 B=0.2±0.4 GSA 6 D=49±9 B=4.5±1 D=155±21 B=48.5±20.6 D=0.5±0.3 B=0.4±0.5 D=2±0.7 B=0.1±0.04 D=1±2 B=2.8±5.7 GSA 5 D=90±25.9 B=2.2±0.7 D=197±49 B=55.5±81 D=3±0.7 B=16.4±16.6 D=0.4±0.8 B=0.1±0.2 D=4±3 B=1.9±0.4 D=2±0.4 B=3.3±2.3 the only chimaera sampled during 2012-2015 MEDITS surveys, by GFCM geographical sub-area (GSA). The codes of these species are shown in Table 2. GSA 1 D=16±17 B=2±1.9 D=296±65 B=53.2±5.2 D=101±55 B=12.8±4.9 D=0.7±0.8 B=0.2±0.21 D=1.2±0.9 B=0.4±0.3 D=2±1.3 B=1±0.9 D=12±6.5 B=1.1±0.7 D=0.2±0.22 B=0.8±1.3 Species Table 3. – Average values (± standard deviation) of density (D, ind. km GALEATL GALUMEL SCYOCAN SCYOSTE GALEGAL MUSTAST MUSTMUS MUSTMED CHIMMON HEPTPER HEXAGRI ALOPVUL CENTGRA CENTUYA SCYMLIC ETMOSPI OXYNCEN SQUAACA SQUABLA SQUTACU

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 89 GSA 25 D=6±3.7 B=9.8±4.7 D=2±0.9 B=3±2.6 D=1±0.8 B=1±0.9 D=0.3±0.6 B=0.2±0.4 D=12±7.5 B=7±3.6 D=0.4±0.3 B=0.2±0.2 D=3±5 B=0.4±0.7 D=10±9 B=2.6±1.8 D=0.1±0.1 B=0.01±0.01 D=0.4±0.7 B=0.6±1 D=1±0.8 B=0.1±0.1 D=0.7±0.8 B=0.02±0.02 D=0.1±0.2 B=0.01±0.02 GSA 23 D=9 B=13.9 D=6 B=6.1 D=3 B=1.8 D=3 B=1.1 D=0.8 B=0.9 D=13 B=5.1 D=12 B=2.2 D=7 B=7.2 D=0.5 B=0.03 D=3.4 B=0.3 GSA 22 D=8 B=11 D=0.8 B=0.2 D=5 B=4.8 D=0.5 B=1.1 D=1.4 B=0.5 D=2.4 B=3.5 D=6 B=3.3 D=5 B=3.7 D=90 B=49.1 D=23 B=3.5 D=17 B=11.1 D=25 B=5.2 D=2 B=0.9 D=1 B=0.2 D=5 B=1.6 D=0.4 B=0.03 GSA 20 D=7 B=7.1 D=2 B=2.7 D=0.2 B=0.1 D=30 B=16.5 D=10 B=1.6 D=0.5 B=0.3 D=0.5 B=0.2 D=4 B=0.4 GSA 19 D=2±1 B=3.3±3 D=0.1±0.15 B=0.4±0.6 D=1±0.7 B=0.8±0.9 D=0.1±0.1 B=0.7±0.8 D=0.4±0.3 B=0.02±0.02 D=2±1 B=0.6±0.5 D=3±1 B=0.3±0.2 D=2±2 B=0.5±0.5 D=0.1±0.1 B=0.04±0.1 D=1±1 B=0.1±0.1 GSA 18 D=0.2±0.2 B=0.3±0.5 D=0.2±0.3 B=0.2±0.4 D=0.1±0.1 B=0.1±0.1 D=0.3±0.2 B=0.3±0.2 D=0.6±0.6 B=0.3±0.3 D=3±1 B=2.5±1.1 D=10±4 B=2±0.8 D=0.8±0.3 B=0.3±0.2 GSA 17 D=0.2±0.2 B=0.28±0.3 D=0.2±0.3 B=0.7±1.2 D=0.4±0.1 B=2±0.8 D=14±14.4 B=42.5±49.6 D=0.8±0.7 B=0.3±0.4 D=11±4 B=11±3.8 D=22±3 B=4±0.4 D=0.1±0.12 B=0.03±0.02 D=0.03±0.1 B=0.02±0.03 D=1.2±0.1 B=0.2±0.04 GSA 16 D=0.3±0.34 B=1.8±2 D=0.1±0.1 B=0.2±0.4 D=0.2±0.1 B=1.3±1.1 D=0.1±0.2 B=2.3±4.6 D=3±2 B=3.8±1.8 D=0.08±0.1 B=0.1±0.2 D=5±1 B=1.2±0.3 D=2±1.4 B=0.8±0.4 D=0.13±0.1 B=0.02±0.02 D=24±8 B=24±3.2 D=55±17 B=10.1±3.6 D=2±1.6 B=0.5±0.3 D=0.2±0.22 B=0.03±0.04 D=0.03±0.1 B=0.03±0.1 D=0.7±0.8 B=4±4.8 D=2±1 B=0.6±0.4 D=0.4±0.4 B=1.2±2.3 D=0.3±0.6 B=0.02±0.04 GSA 11 ) indices estimated in the overall depth stratum (10-800 m, except 10-500 m for GSA 17) all batoids –2 D=0.2±0.3 B=5.7±11.5 D=44±20 B=23.3±9.6 D=2±2.4 B=1.4±1.9 D=8±4 B=9±3.6 D=1±0.5 B=0.4±0.3 D=12±9 B=10.7±6.9 D=41±38 B=26±28.6 D=44±18 B=32.7±12.4 D=32±21 B=4.6±2.2 D=24±15 B=5.1±1.9 D=3±1 B=1.1±0.7 D=0.1±0.3 B=0.1±0.2 GSA 10 D=0.1±0.2 B=0.2±0.3 D=0.5±0.6 B=0.7±0.8 D=0.2±0.2 B=0.2±0.3 D=0.8±0.6 B=0.5±0.5 D=1.7±0.7 B=2±0.9 D=1.2±0.4 B=0.3±0.1 D=0.1±0.2 B=0.01±0.01 D=0.3±0.2 B=0.1±0.1 D=0.03±0.1 B=0.04±0.1 D=0.1±0.1 B=0.03±0.1 GSA 9 D=0.1±0.13 B=0.01±0.01 D=0.1±0.2 B=0.3±0.6 D=0.8±0.3 B=1.5±1.2 D=0.05±0.05 B=0.09±0.1 D=2±1 B=0.8±0.3 D=11±4 B=4.6±1.7 D=2±1 B=0.5±0.3 D=0.1±0.1 B=0.1±0.1 D=3±2 B=0.3±0.1 D=0.02±0.05 B=0.2±0.4 D=2±1 B=0.32±0.3 D=1±0.5 B=0.1±0.05 ) and biomass (B, kg km –2 GSA 8 D=1.5±1.6 B=11.8±9.3 D=0.1±0.2 B=0.9±1.9 D=0.2±0.4 B=0.5±1.1 D=0.5±0.7 B=1.8±2.9 D=26±1 B=24.4±5.7 D=0.2±0.4 B=0.02±0.04 D=4±3 B=4.7±3.3 D=0.1±0.2 B=0.03±0.1 D=251±420 B=65.4±21.1 D=35±13 B=6±2.1 D=0.3±0.4 B=0.1±0.2 D=19±13 B=2.9±2.1 D=0.6±0.8 B=0.7±1.1 D=5±2 B=3.3±1.7 GSA 7 D=0.2±0.3 B=1.5±2.1 D=0.03±0.1 B=0.04±0.07 D=1.3±1.4 B=1.36±1.35 D=1±0.6 B=1±0.7 D=8±4 B=11.1±8.1 D=0.1±0.14 B=0.002±0.004 D=1.3±0.9 B=0.1±0.09 D=2±0.6 B=0.6±0.4 GSA 6 D=0.1±0.2 B=0.7±1.4 D=2±1 B=1.7±1.3 D=2±0.4 B=0.7±0.3 D=3±2 B=1.8±1.2 D=0.4±0.5 B=0.11±0.14 D=1±0.3 B=0.6±0.4 D=1±0.5 B=0.3±0.1 sampled during the 2012-2015 MEDITS surveys, by GFCM geographical sub-area (GSA). The codes of these species are shown in Table 2. GSA 5 D=9±5 B=11.7±9 D=5±4.6 B=6.3±6 D=10±16 B=19.5±33.1 D=2±3 B=2.1±2.6 D=20±7 B=25.7±11.7 D=6±5 B=11.3±11.5 D=53±12 B=45.5±26.9 D=9±39 B=4.6±2.4 D=2±2.4 B=4.5±7.2 D=13±8 B=37.9±35.6 D=7±3 B=11±6.4 D=0.6±1.2 B=5.5±11 D=6±7 B=6.3±5.8 GSA 1 D=0.4±0.6 B=10±13.3 D=0.3±0.4 B=1.5±2.3 D=0.5±0.4 B=1.3±1.7 D=26±29 B=12±14.9 D=5±4.8 B=5.9±4.9 D=0.5±1 B=0.7±1.4 D=0.3±0.5 B=0.01±0.02 D=12±4 B=5±2 D=1±0.8 B=3.5±3.7 D=3±3.5 B=1.9±2.1 Table 4. – Average values (±standard deviation) of density (D, ind. km Species DASICEN DASIPAS DASIVIO MYLIAQU PTEOBOV RAJAOXY RAJACIR RAJAFUL RAJAMEL RAJANAE RAJAAST RAJABRA RAJACLA RAJAMIR RAJAMON RAJAPOL RAJARDA RAJAUND RAJAALB TORPMAR TORPNOB TORPTOR

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A 90 • M.C. Follesa et al. GSA 25 D=11±20 B=0.9±1.5 D=17±24 B=1.6±2.3 D=0.3±0.5 B=0.3±0.6 D=1±1.6 B=3.7±6.4 D=38±67 B=1.5±2.6 D=0.5±0.8 B=0.5±1 D=1±2 B=2.2±3.8 GSA 23 D=2 B=0.4 GSA 22 D=170 B=24.4 D=376 B=43.3 D=7 B=1.8 D=49 B=13.8 D=58 B=33.1 GSA 20 D=5 B=1.5 D=2 B=3.2 D=6 B=4.7 GSA 19 D=3±2 B=0.8±0.7 D=6±7 B=2.4±2.5 D=0.2±0.4 B=1.2±1.3 GSA 18 D=89±28 B=13.3±4 D=0.5±1.1 B=1.4±2.8 D=1.1±0.6 B=0.9±0.2 GSA 17 D=86±18 B=15±2.6 D=3±3 B=2±1.5 D=0.3±0.3 B=0.6±0.5 D=3±2 B=2.6±1.3 D=7±7 B=10.3±10.6 D=10±3 B=9.1±7 D=0.2±0.2 B=0.3±0.4 GSA 16 D=0.1±0.2 B=0.01±0.02 D=88±38 B=13.9±5.1 D=0.3±0.4 B=0.4±0.5 D=12±7 B=9.7±5.1 D=2±2.2 B=2.2±2.1 D=0.4±0.3 B=1.4±1 D=110±37 B=42.1±14 GSA 11 ) indices estimated in the continental shelf stratum (10-200 m) for all sharks and only chimaera sampled D=0.2±0.2 B=0.03±0.04 D=364±84 B=34.3±13 D=0.1±0.2 B=0.21±0.4 D=2±0.5 B=1±0.4 –2 GSA 10 D=6±12 B=0.5±1 D=0.8±0.6 B=0.3±0.2 D=0.2±0.4 B=0.05±0.1 D=0.2±0.4 B=0.2±0.5 GSA 9 D=70±15 B=10.2±3.5 D=0.1±0.2 B=0.4±0.8 D=0.1±0.2 B=0.4±0.7 D=0.3±0.4 B=0.2±0.2 ) and biomass (B, kg km –2 GSA 8 D=52±104 B=1.9±3.9 D=1173±744 B=215±133 D=0.2±0.3 B=0.8±0.6 D=14±24 B=0.3±0.5 D=1±1.1 B=0.3±0.7 GSA 7 D=1.1±1 B=0.4±0.3 D=171±66 B=30±11.3 D=0.1±0.2 B=0.03±0.03 D=1.7±1.8 B=0.36±0.4 GSA 6 D=184±16 B=32±6.7 D=5±10 B=9.6±19.1 during the 2012-2015 MEDITS surveys, by GFCM geographical sub-area (GSA). The codes of these species are shown in Table 2. GSA 5 D=24±32 B=4.6±6.6 D=785±162 B=59.3±12.6 D=36±4 B=158±141 D=22±1.8 B=36.4±22 GSA 1 D=165±28 B=18.7±3.2 Species Table 5. – Average values (± standard deviation) of density (D, ind. km GALEATL GALUMEL SCYOCAN SCYOSTE GALEGAL MUSTAST MUSTMUS MUSTMED CHIMMON HEPTPER HEXAGRI ALOPVUL CENTGRA CENTUYA SCYMLIC ETMOSPI OXYNCEN SQUAACA SQUABLA SQUTACU

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 91 GSA 25 D=21±8 B=29.5±5.4 D=0.3±0.6 B=0.1±0.1 D=0.3±0.6 B=0.2±0.4 D=20±8 B=9.5±1.4 D=2±1 B=1±0.9 D=5±8 B=0.7±1.2 D=5±3 B=1.9±0.9 D=0.2±0.4 B=0.01±0.01 D=1±1 B=0.5±0.5 D=0.3±0.6 B=0.04±0.1 D=0.5±1 B=0.04±0.1 GSA 23 D=24 B=34.3 D=21 B=20.1 D=10 B=3.8 D=1 B=0.1 D=39 B=7.4 D=2 B=0.1 D=3 B=0.3 GSA 22 D=13 B=19 D=2 B=0.4 D=3 B=2.3 D=1 B=0.4 D=3 B=2.6 D=9 B=5.2 D=6 B=3.9 D=97 B=56.1 D=31 B=5.4 D=19 B=14.2 D=16 B=4.4 D=4 B=1.8 D=2 B=0.4 D=10 B=2.6 D=1 B=0.1 GSA 20 D=12 B=12.1 D=33 B=21.7 D=18 B=2.7 D=1 B=0.6 D=1 B=0.5 D=7 B=0.6 GSA 19 D=5±2 B=8.3±7.4 D=0.3±0.4 B=1.1±1.6 D=2±1.9 B=2.2±2.1 D=4±3 B=1.6±1.1 D=7±4 B=0.8±0.6 D=5±4 B=1.1±1.2 D=2±2.3 B=0.2±0.3 GSA 18 D=0.2±0.3 B=0.4±0.8 D=0.3±0.4 B=0.3±0.6 D=1±1 B=0.4±0.5 D=3±2 B=2.9±1.8 D=14±6 B=3±1.2 D=1±0.5 B=0.4±0.4 GSA 17 D=0.2±0.3 B=0.3±0.4 D=0.3±0.33 B=0.7±1.3 D=0.5±0.1 B=2.2±0.8 D=15±16 B=45.7±53.3 D=0.8±0.75 B=0.34±0.41 D=12±4 B=11.7±4.1 D=24±4 B=4.3±0.5 D=0.14±0.1 B=0.03±0.02 D=0.04±0.1 B=0.02±0.04 D=1.3±0.1 B=0.2±0.04 GSA 16 D=1±0.8 B=4.4±5 D=0.1±0.3 B=0.5±1 D=0.6±0.3 B=3.3±2.7 D=0.2±0.4 B=5.7±11.4 D=2±1 B=0.37±0.38 D=3±2.6 B=1.7±1 D=0.3±0.27 B=0.1±0.04 D=46±7 B=48.8±4.7 D=133±41 B=24.5±8.3 D=5±4 B=1.1±0.7 D=0.25±0.3 B=0.06±0.07 D=0.1±0.2 B=0.07±0.1 D=2±2 B=9.6±12.2 D=3±2 B=0.7±0.68 D=0.2±0.3 B=0.01±0.01 D=0.5±1 B=0.06±0.1 GSA 11 D=0.2±0.5 B=9.1±18.1 D=70±31 B=36.8±15.2 D=3±4 B=2.1±3.1 D=4±2 B=6.1±2.6 D=19±14 B=16.8±10.8 D=65±59 B=31.6±27 D=51±21 B=41±17.1 D=50±33 B=7.2±3.5 D=34±21 B=7.8±2.9 D=4±2 B=1.8±1 D=0.2±0.4 B=0.1±1.2 ) indices estimated in the continental shelf stratum (10-200 m) for all batoids sampled during the 2012-2015 the during sampled batoids all for m) (10-200 stratum shelf continental the in estimated indices ) –2 GSA 10 D=0.4±0.4 B=0.5±0.8 D=1.3±1.6 B=1.9±2.2 D=2.1±1.6 B=1.4±1.3 D=0.7±0.6 B=1.3±2.1 D=2.7±1.4 B=0.6±0.4 D=0.2±0.4 B=0.02±0.03 D=0.5±0.3 B=0.1±0.2 D=0.2±0.4 B=0.1±0.2 GSA 9 D=0.1±0.3 B=0.01±0.03 D=0.2±0.4 B=0.5±1.1 D=0.5±0.4 B=1.7±1.8 D=3±1.2 B=1.5±0.4 D=10±3 B=6.1±2.3 D=4±2 B=1±0.5 D=0.1±0.2 B=0.1±0.2 D=3±2 B=0.4±0.3 D=0.1±0.1 B=0.4±0.8 D=3±2 B=0.6±0.5 D=2±1 B=0.2±0.13 ) and biomass (B, kg km kg biomass (B, and ) –2 GSA 8 D=2±5 B=16.2±32.4 D=1.7±2.2 B=5.7±9.2 D=3±7 B=2.3±4.7 D=13±8 B=14.9±10.6 D=753±1333 B=189.2±68 D=113±40 B=19±6.7 D=0.6±1.2 B=0.3±0.6 D=58±40 B=9.5±6.7 D=2±2.2 B=2.4±3.4 D=15±8 B=10.5±5.6 GSA 7 D=0.3±0.4 B=1.8±2.6 D=1±0.7 B=1.2±0.8 D=6±2 B=9±6 D=0.1±0.2 B=0.002±0.01 D=0.1±0.2 B=0.05±0.1 D=3±1 B=0.7±0.5 MEDITS surveys, by GFCM geographical sub-area (GSA). The codes of these species are shown in Table 2. GSA 6 D=0.2±0.4 B=1.1±2.3 D=4±3 B=3.2±2.3 D=3±0.2 B=2.1±0.3 D=5±4 B=3.5±2.6 D=0.8±0.9 B=0.2±0.25 D=2±0.6 B=0.9±0.7 D=3±1 B=0.6±0.2 GSA 5 D=94±72 B=98.3±23.7 D=35±28 B=54±38.5 D=12±23 B=52±97 D=11±21 B=8±16.2 D=53±16 B=68±15.5 D=54±48 B=113±119 D=90±24 B=64.9±24.5 D=91±13 B=50.6±26.7 D=27±33 B=51.9±78.6 D=62±23 B=93.7±15.2 D=65±24 B=111±52.7 D=9±18 B=82.6±165.2 D=26±9 B=33.1±25.4 GSA 1 D=2±2.3 B=27.8±47.4 D=19±25 B=12±18 D=8±7 B=9.2±7 D=1±2 B=1.2±2.5 D=1±2 B=0.03±0.1 D=15±4 B=6±2.9 D=7±8 B=4.6±4.9 Table 6. – Average values (± standard deviation) of density (D, ind. km ind. (D, density of deviation) standard (± values Average – 6. Table Species DASICEN DASIPAS DASIVIO MYLIAQU PTEOBOV RAJAOXY RAJACIR RAJAFUL RAJAMEL RAJANAE RAJAAST RAJABRA RAJACLA RAJAMIR RAJAMON RAJAPOL RAJARDA RAJAUND RAJAALB TORPMAR TORPNOB TORPTOR

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A 92 • M.C. Follesa et al. GSA 25 D=188±83 B=26.8±11.3 D=21±13 B=3±2.1 D=8±4 B=12.8±8.8 D=2±3 B=1.2±2.1 D=420±190 B=14.6±2.2 D=0.7±1 B=1.1±1.8 D=28±46 B=33.6±46.2 D=19±18 B=13.1±11.9 GSA 23 D=85 B=3.6 D=39 B=5.6 D=1.2 B=5.3 D=21 B=17.9 GSA 22 D=289 B=28 D=420 B=41 D=1 B=0.2 D=38 B=16.2 D=8 B=7.8 D=0.1 B=31 D=2 B=6.4 D=0.5 B=0.5 D=74 B=3.7 D=1 B=0.5 D=73 B=17.3 D=133 B=80.8 D=1.5 B=14.8 GSA 20 D=126 B=12.6 D=24 B=3.5 D=2 B=2.3 D=4 B=15.4 D=3 B=0.05 D=2 B=1.3 D=55 B=26.6 GSA 19 D=193±76 B=32.1±12.1 D=14±9 B=1.7±1.3 D=4±2 B=1±0.7 D=0.06±0.1 B=0.05±0.1 D=0.06±0.1 B=0.03±0.1 D=1±0.3 B=1±0.3 D=77±45 B=3.3±1.1 GSA 18 D=305±154 B=38.8±15.2 D=61±11 B=2.8±0.3 D=17±6 B=5.8±2.2 D=0.2±0.2 B=0.2±0.2 D=0.1±0.2 B=0.3±0.6 D=2±0.6 B=3.5±3.1 D=157±98 B=5.6±1.6 D=3±5 B=0.5±0.4 GSA 17 D=51±31 B=4.9±2 D=13±12 B=1±0.9 D=0.5±1 B=0.2±0.4 D=5±10 B=0.2±0.4 D=0.1±0.3 B=0.6±1.2 GSA 16 D=244±114 B=37.3±15.9 D=41±30 B=5.3±3 D=0.04±0.1 B=0.9±1.7 D=0.1±0.2 B=0.09±0.1 D=7±4 B=2.6±1.1 D=0.4±0.2 B=0.6±0.57 D=0.05±0.1 B=0.4±0.7 D=2±1.9 B=6.1±5.3 D=0.04±0.07 B=0.1±0.2 D=3±1 B=5.8±2.3 D=35± 9 B=2.8±1 D=0.1±0.2 B=0.1±0.2 D=28±4 B=14.8±2.5 ) indices estimated in the slope stratum (201-800 m, except 201-500 m for GSA 17) all sharks –2 GSA 11 D=542±174 B=38.1±14.7 D=471±201 B=15.3±5.6 D=1±0.8 B=0.1±0.03 D=0.1±0.2 B=0.05±0.1 D=0.6±0.4 B=0.2±0.1 D=108±28 B=6±0.5 D=0.1±0.2 B=0.1±0.3 D=7±4 B=4.7±3 GSA 10 D=207±67 B=28.2±8.3 D=7±3 B=1±0.4 D=0.8±0.6 B=0.1±0.1 D=0.9±0.3 B=1±0.8 D=33.7±7.9 B=2.3±0.4 D=1.9±2.1 B=0.6±0.5 ) and biomass (B, kg km –2 GSA 9 D=439±75 B=47.9±9.1 D=102±31 B=8.2±2.4 D=4±1.3 B=0.9±0.2 D=0.1±0.2 B=0.3±0.7 D=0.41±0.4 B=1.4±1.3 D=0.6±0.2 B=0.4±0.3 D=77±24 B=5.9±1.6 D=20±19 B=3±2.9 GSA 8 D=2319±659 B=103.5±14.3 D=322±67 B=28.3±8.7 D=0.1±0.2 B=0.4±0.7 D=0.4±0.9 B=1.2±2.4 D=2±0.4 B=0.8±0.6 D=222±123 B=5.7±2.8 D=0.3±0.4 B=0.2±0.23 D=99±62 B=47.5±20.7 GSA 7 D=1573±952 B=208±97.8 D=90±58.4 B=10.9±3 D=80±32 B=29.4±13 D=0.2±0.4 B=0.6±1.2 D=34±22 B=1.6±1.1 D=0.4±0.8 B=1.3±2.7 D=0.6±1 B=1.2±1.9 D=0.5±1 B=1.2±2.3 GSA 6 D=228±42 B=20.8±4.7 D=59±19 B=10.6±3.3 D=1.1±0.8 B=0.9±1.2 D=3.7±0.9 B=0.3±0.03 D=0.3±0.6 B=0.4±0.8 GSA 5 D=705±153 B=20.4±5.6 D=316±174 B=24.2±3.5 D=3±7 B=0.7±1.3 D=40±23 B=24.5±9.1 D=3±6 B=4.1±8.3 and the only chimaera sampled during 2012-2015 MEDITS surveys, by GFCM geographical sub-area (GSA). The codes of these spec ies are shown in Table 2. GSA 1 D=180±184 B=22.1±21.9 D=2663±585 B=479±46.8 D=84±67 B=11.8±4.7 D=1.6±1.9 B=0.4±0.5 D=3±2 B=1.1±0.8 D=13±7 B=6.2±2.2 D=131±71 B=12.3±7.3 D=0.5±0.6 B=1.9±3.1 Species GALEATL GALUMEL SCYOCAN SCYOSTE GALEGAL MUSTAST MUSTMUS MUSTMED CHIMMON HEPTPER HEXAGRI ALOPVUL CENTGRA CENTUYA SCYMLIC ETMOSPI OXYNCEN SQUAACA SQUABLA SQUTACU Table 7. – Average values (± standard deviation) of density (D, individuals km

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 93 GSA 25 D=2±3 B=4.2±5.4 D=3±1 B=3.8±3.7 D=1±1 B=1.2±1.1 D=10±8 B=6.1±4.2 D=3±5 B=0.3±0.5 D=12±11 B=2.7±2.5 D=0.6±1 B=0.7±1.2 D=1±1 B=0.04±0.04 D=1±1 B=0.02±0.02 GSA 23 D=2 B=5.2 D=5 B=2.6 D=1.2 B=1.3 D=18 B=7.3 D=10 B=10.4 D=4 B=0.2 GSA 22 D=4 B=5.1 D=6 B=6.6 D=0.3 B=0.5 D=2 B=0.5 D=2 B=0.8 D=4 B=1.9 D=4 B=3.4 D=84 B=44 D=17 B=2 D=16 B=8.8 D=31 B=5.7 D=2 B=0.8 GSA 20 D=4 B=6.4 D=1 B=0.4 D=27 B=9.2 D=1 B=0.03 GSA 19 D=0.2±0.2 B=1.1±1.4 D=1±1 B=0.03±0.04 D=0.2±0.22 B=0.02±0.02 D=0.3±0.3 B=0.07±0.1 D=0.15±0.2 B=0.1±0.12 GSA 18 D=0.3±0.2 B=0.2±0.2 D=1±0.7 B=1±0.7 D=2.8±0.8 B=1.7±0.6 D=0.1±0.2 B=0.01±0.03 GSA 17 D=0.1±0.3 B=0.04±0.1 D=3±2.8 B=0.9±0.6 D=0.5±0.6 B=0.03±0.04 D=0.1±0.3 B=0.01±0.01 GSA 16 D=5±3 B=6.3±3 D=0.1±0.2 B=0.26±0.3 D=7±2 B=1.8±0.6 D=1±2 B=0.1±0.3 D=10±12 B=7±6.9 D=2±2 B=0.4±0.5 D=0.3±0.33 B=0.05±0.1 D=0.1±0.3 B=0.02±0.04 D=0.1±0.1 B=0.25±0.3 D=1±0.8 B=0.2±0.1 D=0.5±0.52 B=2±3.9 GSA 11 ) indices estimated in the slope stratum (201-800 m, except 201-500 m for GSA 17) all batoids sampled D=14±9 B=14±5.3 D=2±1 B=1±0.8 D=31±16 B=18.1±4.6 D=0.1±0.1 B=0.01±0.03 D=7±3 B=0.5±0.3 D=0.2±0.3 B=0.01±0.02 –2 GSA 10 D=0.3±0.3 B=0.3±0.5 D=0.1±0.1 B=0.01±0.02 D=2±1 B=2.3±1.1 D=0.4±0.3 B=0.1±0.1 D=0.1±0.2 B=0.02±0.04 D=0.1±0.1 B=0.1±0.1 GSA 9 D=1±0.5 B=1.2±1.4 D=0.1±0.12 B=0.2±0.2 D=0.2±0.4 B=0.04±0.1 D=13±6 B=2.9±1.3 D=2±1 B=0.2±0.1 D=0.3±0.4 B=0.06±0.1 D=0.4±0.3 B=0.1±0.04 ) and biomass (B, kg km –2 GSA 8 D=2.4±4.5 B=17.6±27 D=0.1±0.3 B=1.4±2.8 D=0.3±0.6 B=0.8±1.6 D=37±2 B=34.5±6.3 D=0.3±0.6 B=0.03±0.06 D=0.1±0.2 B=0.04±0.07 D=21±7.6 B=8.8±3.5 D=0.2±0.5 B=0.01±0.01 D=2±1.3 B=0.03±0.02 D=0.1±0.2 B=7E-4±1E-3 GSA 7 D=0.2±0.3 B=0.2±0.4 D=7.1±7.2 B=7.3±7.2 D=20±9 B=20.2±15.8 D=6.3±4.6 B=0.3±0.2 GSA 6 during the 2012-2015 MEDITS surveys, by GFCM geographical sub-area (GSA). The codes of these species are shown in Table 2. D=0.4±0.9 B=0.1±0.3 D=1.4±1.2 B=0.3±0.4 D=2±3.2 B=1.4±2.5 D=1±0.8 B=0.2±0.22 GSA 5 D=0.5±0.9 B=10.8±21.6 D=26±10.1 B=36.1±17.3 D=8±9.4 B=13.5±17.2 D=13±5 B=79.2±37.8 D=88±29 B=52.9±38.4 D=11±2.6 B=28.4±11.6 GSA 1 D=0.5±0.9 B=10.8±21.6 D=1.1±1.9 B=6.8±10.2 D=4±3 B=6.4±6.9 D=31±29 B=10.3±11.6 D=2±1 B=0.7±0.3 D=2±1 B=6.6±6.9 Table 8. – Average values (± standard deviation) of density (D, ind. km Species DASICEN DASIPAS DASIVIO MYLIAQU PTEOBOV RAJAOXY RAJACIR RAJAFUL RAJAMEL RAJANAE RAJAAST RAJABRA RAJACLA RAJAMIR RAJAMON RAJAPOL RAJARDA RAJAUND RAJAALB TORPMAR TORPNOB TORPTOR

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A 94 • M.C. Follesa et al.

Fig. 3. – Pearson correlation matrix of the covariates used in GAM models for abundance of sharks (including also Chimaera monstrosa and Squatina aculeata) and batoids: Geographical coordinates (X: longitude; Y: latitude), depth, bottom slope, sea bottom temperature (BotTemp) and sea surface temperature (SST). tween the GSAs (Tables 3 and 4). Two species mainly family was the most abundant, being represented in contributed to shark abundance in the basin (Table 3): primis by R. clavata and then by R. miraletus, R. poly- G. melastomus, with the highest abundances in GSA8 stigma and R. asterias (Table 6). T. marmorata reached (1607±35 ind. km–2 and 71.6±9.2 kg km–2) and the lower values than the above species, as did Dasyatis lowest ones in GSA 17 (4±2 ind. km–2 and 0.3±0.1 pastinaca, which showed high values only in GSAs 5 kg km–2), and S. canicula, with the highest density and 11. (589±260 ind. km–2) and biomass (86.9±42.1 kg km–2) The slope, with a high abundance of sharks, is in GSA8 and the lowest ones in GSA 10 (4.8±1.7 ind. characterized by the abundance of G. melastomus in km–2 and 0.8±0.3 kg km–2). The third most abundant all GSAs (Tables 7 and 8), followed by S. canicula, E. shark was E. spinax (Table 3), with the highest values spinax and S. blainville. Among batoids, the Rajidae in GSA 25 (5337±140 ind. km–2 and 12±1.5 kg km–2) family was mostly represented by R. clavata and D. and the lowest ones in GSA 17 (0.4±0.7 ind. km–2 and oxyrinchus. Myliobatidae was the only family not pre- 0.02±0.03 kg km–2). Among batoids, R. clavata was sent on the slope, while Dasyatidae and Torpedinidae the most abundant species, followed by R. miraletus were scanty (Table 8). The highest abundance of C. (Table 4). Although T. marmorata was frequently en- monstrosa was registered in GSA 7 (80±32 ind. km–2 countered in all GSAs, its density and biomass were and 29.4±13 kg km–2). This species was not widespread very low (≤12 ind. km–2 and ≤6.3 kg km–2, respec- throughout the Mediterranean (Table 7). tively; Table 4). Focusing on the continental shelf, apart from S. GAM results canicula, which was the most abundant chondrichthyan species caught in all GSAs (Tables 5 and 6), except for The covariates tested for collinearity showed no GSAs 10 and 19, this macro-stratum was characterized VIF values greater than 3 and none of the Pearson by the prevalence of batoids. Among these, the Rajidae correlation coefficients was greater than 0.5 (Fig. 3).

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 95

Table 9. – GAM models for density (D, ind. km–2) and biomass (B, kg km–2) indices of sharks (squa) and batoids (bat), considering geographical coordinates (Lon, Lat), depth, sea surface temperature (SST) and sea bottom temperature (BotTemp) as explanatory variables; i is the index of the i-observation; α is the intercept; εi is the error term; AIC is the Akaike information criterion; Dev. Expl. is the deviance explained. Model AIC Dev. Expl. (%)

log(D_squa)~α+fi (Loni, Lati)+fi (depthi)+fi (SSTi)+fi (BotTempi)+εi 16494.2 58.3

log(B_squa)~α+fi (Loni, Lati)+fi (depthi)+fi (SSTi)+fi (BotTempi)+εi 14561.8 49.2

log(D_bat)~α+fi (Loni, Lati)+fi (depthi)+fi (SSTi)+fi (BotTempi)+fi (slopei)+εi 15318.3 43.8

log(B_bat)~α+fi (Loni, Lati)+fi (depthi)+fi (SSTi)+fi (BotTempi)+fi (slopei)+εi 14712.9 41.2

Fig. 4. – Smoother for the covariates estimated from GAM models for abundance of sharks and batoids: depth, sea surface temperature (SST), sea bottom temperature (BotTemp) and bottom slope.

As no collinearity was detected, none of the covari- encing the distribution of both sharks and batoids, ates was rejected from the model analysis. The bot- but the models described a different pattern for each tom slope was not used in the models for sharks, as group. Density and biomass of sharks increased with the estimated degrees of freedom values were close to depth, the highest values of the response variables be- one, giving no contribution to the improvement of the ing estimated around the Balearic Islands (GSA 5), GCV and Aikake information criterion values of the off the northeastern Iberian Peninsula (GSA 6), the models. The estimated degrees of freedom of the esti- eastern coast of Corsica (GSA 8), the northeastern mated smoothers used in each model were significantly coast of Sardinia (GSA 11), the northern part of GSA different from zero (p-value<0.05). 16, the southern part of GSA 19, around the pit of The best models for abundance of both sharks and Bari in GSA 18 and the northern part of GSA 22. The batoids are shown in Table 9 and Figure 4. The residu- models for batoids showed a very similar distribution als of the models were tested with the Shapiro-Wilk pattern to that of sharks, but with hotspots at lower normality test (p<0.05). Although the residuals of all depth. The highest values of the response variables the models were not normally distributed (Fig. 5), they were estimated close to the Balearic Islands (GSA could be considered symmetric enough as their skew- 5), off eastern Corsica (GSA 8), off northern Sar- ness index was in the ±1 range (Hair et al. 2010). The dinia (GSA 11) and in the Strait of Sicily (GSA 16). predictive maps resulting from the models are shown Temperature had a significant influence in the species in Figure 6. distribution, but with an opposite pattern in the two In each group of species, the effect of the covari- groups. The SST was lower in the areas of highest ates is quite the same between density and biomass biomass of sharks than in the areas of highest biomass (Fig. 4). Depth was the most important driver influ- of batoids.

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Fig. 5. – Plots and histograms of the residuals of GAM models for density (ind. km–2; left) and biomass (kg km–2; right) indices for sharks (top; including also Chimaera monstrosa and Squatina aculeata) and batoids (bottom).

Fig. 6. – Maps from GAM models of density (ind. km–2; left) and biomass (kg km–2; right) indices for sharks (top; including also Chimaera monstrosa and Squatina aculeata) and batoids (bottom).

DISCUSSION Algeria, and Tunisia (between 57 and 69 species), and slightly lower in the northwestern countries, Spain This work provides standardized and updated in- (including the Balearic Islands), France and Italy, and formation regarding the distribution, occurrence and in Greece. Intermediate levels of diversity have been abundance of chondrichthyans along the whole north- reported for the central Mediterranean along the coasts ern Mediterranean, from west to east. Historically, their of Libya, Malta and Sicily and in the coastal waters diversity has been considered greatest in the western of the countries bordering the Adriatic and Aegean basin, particularly in the coastal waters of Morocco, seas. This longitudinal gradient in the species richness

SCI. MAR. 83S1, December 2019, 81-100. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04998.23A Chondrichthyes in the North Mediterranean • 97 of chondrichthyans has been suggested by several au- in the fact that this bathyal species has a lower degree thors (Dulvy et al. 2016, Melendez et al. 2017, Bradai of survival after discarding than other chondrichthyans et al. 2018). However, the results of the present work (Ferretti et al. 2005). Among the rare or non-common seemed not to confirm this pattern (the Strait of Sicily species found in our analysis, the order Squatiniformes and the Aegean showed the largest number of species), deserves particular attention. In the last century, an- probably because North Africa is not involved in the gelsharks (Squatina spp.) were common in Mediter- MEDITS programme, so diversity in this area could ranean waters (Ferretti et al. 2005). Fortibuoni et al. not be considered. (2016) reported that Squatina squatina was commonly Comparing our results with the literature, an abrupt sold in the main fish markets until the collapse of this decrease in the number of chondrichthyan species has species during the 1960s led to its disappearance in the been detected in the northern Adriatic (GSA 17): from northern Adriatic. Furthermore, Damalas and Vassilo- 33 demersal, meso-predatory elasmobranchs in 1948 poulou (2011) underlined the absence of the two other (Jukić-Peladić et al. 2001, Coll et al. 2009, Ferretti et species of angelsharks, Squatina aculeata and Squati- al. 2013) to 20 species found in this study during recent na oculata in the Aegean Sea, reporting that they had years (2012-2015). This confirms the known structural not been present in the catches for a couple of years. depletion of the demersal community in terms of diver- Our study shows that the only species of angelsharks sity during the last few decades. caught during recent years is S. aculeata, whose pres- A previous study conducted by Bertrand et al. (2000) ence has been detected in the same area. This fact could involving almost the same GSAs sa those analysed in be a clear sign of decline or indicate a possible risk of this work pointed out that the most widespread species extinction of this and the other two congeners in the in the basin were Scyliorhinus canicula, Galeus me- Mediterranean, although, fortunately, interviews with lastomus and Etmopterus spinax among sharks, Raja fishermen reveal that this species seems not to have clavata, Raja miraletus, Raja asterias and Torpedo been extirpated yet, as was also observed in the Tyr- marmorata among batoids, and Chimaera monstrosa. rhenian (Ligas et al. 2013). From our results, the most frequent and abundant spe- Some authors have reported the spiny dogfish (Sq- cies all around the basin are S. canicula, G. melasto- ualus acanthias) to be one of the most frequent shark mus and E. spinax among sharks and R. clavata and R. species captured in the Mediterranean (Baino et al. miraletus among batoids, partly confirming the find- 2001, Serena et al. 2009, Damalas and Vassilopoulou ings of Bertrand et al. (2000) and other previous data 2011). Our results show its congener S. blainville to be present in literature for the western (Barría et al. 2015, the most abundant Squaliformes species throughout the Ramírez-Amaro et al. 2015, Marongiu et al. 2017), basin, particularly in its central-western part (eastern central (e.g. Jukić-Peladić et al. 2001, Bottari et al. Corsica and southern Sicily) and in the eastern Ionian 2014) and eastern (Tserpes et al. 2013) Mediterranean. and Aegean seas. Tsagarakis et al. (2013) also reported The overlapping of observations should probably be S. blainville to be one of the most important species of attributed to the higher fishing pressure resilience of the demersal assemblages in the eastern Ionian Sea. The these species (Cavanagh and Gibson 2007). Usually, reason for this replacement between these two sharks in spite of this resilience, fishing pressure should be could be related to taxonomic problems afflicting the considered as one of the main variables affecting the Squalus genus and currently solved in different areas abundance of chondrichthyans, but not the only one of the Mediterranean (Bonello et al. 2016, Bellodi et al. (Frodella et al. 2016). 2018). Indeed, recent studies (e.g. Anastasopoulou et To parameterize fishing pressure is a big issue, par- al. 2018) highlight that S. acanthias showed a limited ticularly in terms of spatial distribution and at a large geographic distribution in the past, suggesting an inac- scale (Russo et al. 2017). In fact, although a spatial curate classification of these two species. index of fishing pressure was recently developed at Focusing on macro-areas, previous findings a regional scale in the Mediterranean (Kavadas et al. regarding the distribution and abundance of chon- 2015), the only maps of the geographical distribution drichthyan species conducted in Spanish and French of trawling fishing pressure along the whole basin were seas (Massutí and Moranta 2003, Gouraguine et al. drawn up by Sbrana et al. (2013). The comparison be- 2011, Ramírez-Amaro et al. 2015) are, in general, tween these maps and our results shows that the areas confirmed in the present work, with the highest under higher fishing pressure, such as the Adriatic and number of species and abundance around the Balear- the Spanish coast (with the exception of the Balearic ic Islands. Regarding Italian seas, our data, as in a Islands) show low abundance of chondrichthyans, but study conducted by Relini et al. (2000), confirmed other areas with a high level of fishing pressure, such the highest diversity of demersal chondrichthyans as southwestern Sicily, show a high abundance of these in the Strait of Sicily. Furthermore, G. melastomus species. This finding suggests that other environmental and S. canicula were shown to be the most frequent drivers work together with fishing pressure to shape and abundant shark species in Spanish and Italian their distribution. waters, and R. clavata and R. miraletus were the Some differences must be noted between our results most frequent and abundant batoids. In particular, and those of Bertrand et al. (2000), especially regarding R. clavata, with a large depth range, seemed to be the decline of C. montrosa. This species seemed not so more abundant off the Balearic Islands (GSA 5) and widespread as in the past years. A possible explana- western Italy (GSAs 9, 10 and 11), where the slope tion for this decline, besides its rarity, could be found is wide. R. miraletus, which is mostly widespread on

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Bertrand J.A., Gil de Sola L., Papaconstantinou C., et al. 2000. An sandy bottoms of the shelf, is also abundant around international bottom trawl survey in the Mediterranean: the the Balearic Islands and off the central part of Italy Medits programme. In Bertrand J.A., Relini G. (eds). Demersal (GSAs 16, 17, 18, 19), particularly in the northern resources in the Mediterranean. Proceedings of the symposium Adriatic (GSA 17), where it is the most abundant held in Pisa, 18-21 March 1998. Actes de Colloques 26: 76- 93. Ifremer, Plouzane. skate. The low depth of this area, where the slope Bertrand J.A., Gil De Sola L., Papaconstantinou C., et al. 2002. The represents only 7% of its surface, explains the scant general specifications of the Medits surveys. Sci. Mar. 66: 9-17. presence or the absence of the deep species E. spi- https://doi.org/10.3989/scimar.2002.66s29 Bonello J., Bonnici L., Ferrari A., et al. 2016. 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